333 results about "Generative Design" patented technology

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

A method and system for designing curing processes is disclosed. The method includes obtaining design requirements using a requirement gathering agent, determining potential ranges of recipes and operating conditions with an operations specialist agent, identifying suitable material options and assessing corresponding properties with a material specialist agent, defining dimensions and shape aspects of the design using a design specialist agent, extracting and reasoning relevant information with a knowledge processing and retrieval agent, formulating a final requirement specification with an experiment enabler agent, generating a first set of experiments based on the final requirement specifications using an experiment designer, performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions, updating, by a predictive model optimizer agent implementing a continual learning framework, the prediction model in real-time; and optimizing and updating, by a process optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

A computerized method for generating a 3D Computer-Aided Design (CAD) is provided. The method comprising: receiving an input comprising requirements for a new design; processing the input to generate a CAD design using a trained AI model, wherein the CAD design is an assembly of at least two separate components; and outputting the CAD design.

In various embodiments, a computer-implemented method for performing an analysis of a sketch to identify one or more objects for a generative design, the method comprising receiving the sketch via a user interference, executing a first trained machine learning (ML) model that generates an identification of a first object included in the sketch based on one or more contextual features that are associated with the first object and also are included in the sketch, transmitting the identification to a second machine learning model, and executing a second trained ML model that generates a first design object based on the identification.

An apparatus and method for determining and solving design problems is illustrated herein. Apparatus includes a processor and a database of components by manufacturer. The processor is configured to receive a representative part model which may include 2D prints and 3D models of a building design. The processor is configured to identify and categorize the representative part model to a design problem and generate design solutions to solve the design problem. The processor is also configured to encode layers of required information for a first machine-learning module. The processor determines components from the database of components, as a function of the design solution.

In various embodiments, a computer-implemented method for generating a design object comprises combining at least two of a design intent text or one or more non-textual inputs to generate a multimodal prompt, executing a trained machine learning (ML) model on the multimodal prompt to generate a design object, and displaying the design object in a design space.

The invention provides a BIM technology and project amount intelligent verification method and system, and the method comprises the steps: carrying out the word segmentation and part-of-speech tagging of a progress log, extracting an excavation region, a completion amount and a timestamp of the day, and obtaining a construction progress information set; according to the construction progress information set, identifying a semantic relationship of an excavation area, a completion volume and a time triple, and determining time-space semantic representation; extracting geometric information of each target earthwork logic body from the set of earthwork logic bodies, and calculating a design engineering quantity of each associated logic body in a corresponding preset elevation range in real time in combination with corresponding elevation data extracted from the BIM model to generate a design quantity data set; and according to the early warning result, updating the state attribute of the earthwork logic body in the BIM model, generating a dynamically updated BIM model state in combination with the deviation occurrence timestamp and the difference value, extracting the spatial distribution of the earthwork logic body in the BIM model, and generating a construction path.

The invention provides an interactive system virtual test method and device based on multi-mode cognitive twin bodies. The method comprises the following steps: 1) constructing user cognitive twin: outputting a behavior decision tree with confidence through an adversarial generative network based on population attributes, cognitive preferences and behavior history; 2) creating a dynamic interaction environment: encoding a to-be-tested interface into a semantic graph structure which can be analyzed by a machine, and associating a physical operation constraint rule base; 3) running a multi-agent test: deploying a twin cluster in the virtual sandbox, and recording an operation path, an attention thermodynamic diagram and decision delay; and 4) generating a design defect map, namely, positioning an interface cognitive friction point by comparing the deviation degree of an expected interaction flow and an actual behavior track. According to the method, the problems that a traditional user test period is long and scene coverage is limited are solved, and early interception of interaction risks and design optimization preposition are achieved. The method can be widely applied to high-risk / high-cost / high-cycle test scenes of the man-machine interaction system.

Example embodiments may include: obtaining a design specification of a graphical user interface (GUI), wherein the design specification is compatible with a GUI design tool and includes a layout of design artifacts; generating, through use of a component mapper, respectively corresponding GUI components for the design artifacts in accordance with the layout; populating, based on properties of the design artifacts, respectively corresponding properties of the GUI components; and generating, based on the layout, the GUI components, and their corresponding properties, a deployable software implementation of the GUI.

The invention relates to the technical field of gift packaging three-dimensional modeling, and discloses a construction method of a gift box packaging design three-dimensional model. The method comprises the following steps: collecting natural language description, a freehand sketch image and a physical material attribute parameter; analyzing a natural language to construct a design intention map, and analyzing a sketch to generate a geometric feature code; performing knowledge alignment fusion on the two to form enhanced design semantic representation; generating an initial three-dimensional design framework by using a generative design algorithm; material parameters are integrated, compatibility simulation test is carried out, parameter self-adaptive adjustment circulation is started according to a feasibility evaluation report, and component parameters are dynamically optimized; and rendering and outputting a final model. Through deep fusion of multi-modal design information and preposed dynamic optimization of physical attributes, the accuracy of design intention expression and the producibility of a three-dimensional model are improved, and automation and intelligentization of a design process are realized.

The invention belongs to the related technical field of composite material additive manufacturing, and discloses a composite material microstructure reverse design method and system based on a generative model, and the method comprises the steps: constructing a sample data set corresponding to a microstructure image-attribute parameter of a composite material; performing conditional embedding enhancement on the unconditional generation model, constructing a conditional embedding generation model, training the conditional embedding generation model by using a sample data set corresponding to a microstructure image-attribute parameter, the attribute parameter being an embedding condition during image generation, and obtaining a trained model as a reverse design model; and generating a design microstructure image by using a reverse design model according to the expected attribute parameters. According to the method, a reverse design process of quickly and accurately generating a corresponding vivid microstructure according to expected attributes is realized on the basis of powerful image generation and conditional embedding capability of the generative model, and the problems of long design period and uncontrollable effect are solved.

A phononic crystal generative design method based on a diffusion model and an unstructured polygonal mesh belongs to the field of phononic crystal design, and comprises the following steps: firstly, generating phononic crystal unit cells, calculating a dispersion curve of the phononic crystal unit cells, and constructing a data set; secondly, constructing a diffusion model and designing a neural network to learn a reverse process of the diffusion model; inputting the training data into the neural network for training until a termination condition is met; and finally, extracting a dispersion curve from the test data set as a reverse design target, quickly generating a group of photonic crystals by using a neural network and a diffusion model sampling algorithm, calculating the dispersion curve corresponding to the generated photonic crystals, and calculating an error between the dispersion curve and the reverse design target. According to the method, the dependence of an existing generative design method on a rule design domain can be broken through, and the adaptability to irregular geometrical shapes and complex boundaries is improved; efficient learning of the complex mapping relation between the mechanical attributes and the geometric configuration is achieved; the method has the advantages of high design speed and high precision.

The invention discloses a method for constructing a hyperbolic discrete diffusion model with a three-dimensional RNA (Ribonucleic Acid) structure inversely folded, which comprises the following steps of: embedding rich geometrical characteristics and other topological properties of the RNA three-dimensional structure into a hyperbolic space; a forward diffusion process is utilized to carry out step-by-step noise addition on a real one-dimensional sequence of an RNA three-dimensional structure and carry out step-by-step noise removal on a hyperbolic isovariant graph neural network noise removal model, and on the basis of a limited training sample, nucleotide distribution of a target RNA three-dimensional structure, namely the one-dimensional sequence, is effectively recovered, so that the target RNA three-dimensional structure is obtained. According to the method, the hierarchical structure of RNA is represented through a hyperbolic isovariant graph neural network denoising model, and the structural similarity measurement of three-dimensional reverse folding is enhanced. The invention provides a powerful artificial intelligence method for the generative design of a functional RNA structure, and is beneficial to the design of RNA drugs and RNA biomolecules and the application of the RNA drugs and RNA biomolecules in biological medicines.

Systems and processes enhance cybersecurity by dynamically generating a decoy sub-application that operates parallel to the primary application using generative design and a large language model. The core feature involves real-time anomaly detection within application traffic, utilizing AI to assess threats and orchestrate appropriate responses. Upon identifying potential security threats, the system employs generative design principles to architect a restricted-functionality sub-application, deploying it instantly to engage and analyze the attack vectors without compromising sensitive production data. The sub-application is isolated through software-defined networking, ensuring that its operations do not affect the primary application's functionality. Additionally, sophisticated traffic redirection mechanisms are employed to divert suspicious traffic from the primary to the decoy application, thereby protecting the integrity while allowing detailed threat analysis. This dual-capability system not only safeguards against disruptions but also enhances adaptive security measures through continuous learning and system adjustments.

An apparatus includes a processor configured to receive, during editing of a first design, user input indicating that a first design element has a first content role. The processor is configured to generate a content signature of the first design indicating that the first design element has the first content role, to generate a second design based on a design template, and to update the second design by applying the content signature to the second design. Generating the second design includes, based on determining that the design template includes a second design element having the first content role, adding a third design element having the first content role to the second design. Applying the content signature to the second design includes transferring content from the first design element to the third design element. The processor is configured to generate a graphical user interface including an image of the second design.

The invention belongs to the technical field of automobile structure design, and particularly relates to a large-model-based automobile body structure generative design method and system, and the method comprises the steps: building a multi-source database containing automobile body structure pictures, design variables and mechanical properties; training a vehicle body structure performance evaluation model and generating an evaluation model according to the database; on the basis of the generated evaluation model, the general text graph large model is finely adjusted, and a vehicle body structure generative design large model is obtained; generating a structure picture by using the obtained large model, and establishing a structure size evaluation model in combination with a database; inputting design requirements and constraint conditions, generating a vehicle body structure, evaluating the performance and size of the vehicle body structure, and outputting a final result. According to the method, the problems of low efficiency and strong dependence on experience of engineers in an existing automobile concept design stage are solved, the efficiency and creativity of automobile body structure design are improved, the design period is shortened, manual intervention is reduced, and automation and intelligence of automobile body structure design are promoted.

The invention discloses a digital research and development demand automatic disassembling method based on knowledge reasoning. The method comprises the following steps: gathering multi-source demands, uniformly de-duplicating, and establishing a version baseline; a semantic graph is generated according to the standardized requirements, a support graph and a constraint set are constructed, and a resource portrait, historical measurement and a template library are formed; performing retrieval and sub-graph aggregation by using an improved GraphRAG model, completing alignment reasoning and outputting a fact set, a conflict list and a deletion list; the method comprises the following steps: generating a hierarchical task, dependency and priority by using an improved Reflexion algorithm, and performing shadow scheduling and acyclic verification to obtain an execution sequence table; generating a design / interface / test / verification / acceptance workpiece, and solving resources, milestones and key paths according to the design / interface / test / verification / acceptance workpiece; and a tracing link and an early warning mechanism are established, and rearrangement and resource redistribution are carried out when the time exceeds the limit. According to the method, the schedule forming time is shortened, the reworking and delay risks are reduced, and the workpiece coverage rate, the critical path controllability and the delivery traceability are improved.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a design space, design criteria, and boundary conditions for numerical simulation; defining an application of the boundary conditions to a voxelized mesh, including specifying a distribution of a load to nodes of voxels in the voxelized mesh that correspond to a surface of preserve geometry; and iteratively modifying a generatively designed three dimensional shape in the design space in accordance with the design criteria and a physical response of the modeled object determined by the numerical simulation performed using the application of the boundary conditions to the voxelized mesh, wherein the distribution of the total loading value during determination of the physical response ensures an equivalence between the total loading value and a sum of loading values distributed to the respective nodes of the voxels that correspond to the surface.

The invention discloses an intelligent building decoration design method based on deep learning, and the method comprises the following steps: S1), employing a monocular camera to shoot pictures of a building at all angles, and obtaining an original picture; s2) extracting keywords, and making cue words; s3) building decoration data collection; s4) performing data screening and preprocessing; s5) customizing a loss function, and training the diffusion model to obtain a trained diffusion model; s6) inputting the original image into the trained diffusion model to generate a preliminary image; inputting the preliminary image and the cue word into a pre-trained conditional generative adversarial network to obtain a preliminary design drawing; and S7) inputting the preliminary design drawing into a pre-trained variational auto-encoder to obtain an architectural decoration design reference draw.According to the intelligent architectural decoration design method based on deep learning, through the design of the self-defined loss function and the dynamic weight, in combination with the deep learning model, the image is automatically processed, and the design reference drawing is generated, so that the working efficiency is improved.

A generative machine learning model, such as a convolutional neural network (CNN), can be trained with solutions from a topology optimization solver for a solution for a topology of a set of structures so that the generative machine learning model can generate a plurality of alternative designs for a structure that are alternative topology optimizations (for the structure) for a set of initial setup parameters. The generative model when being trained includes a generative network and a discriminator network. The generative model can be trained using outputs from a CNN autoencoder for densities and a CNN autoencoder for strain energies.

The invention discloses a 3D printing self-adaptive wing design method based on generative artificial intelligence, and relates to the crossing field of 3D printing and artificial intelligence. In order to solve the technical problems of complex manufacturing, low reliability and large weight of a traditional process in the prior art, the technical scheme provided by the invention comprises the following steps: pre-training a large language model to obtain a trained basic model; receiving self-adaptive wing structure design parameters input by a user and generating a design scheme script; calling three-dimensional modeling software, automatically establishing a wing three-dimensional model based on the design scheme script, and outputting a model file; calling simulation analysis software, performing wing performance simulation analysis based on the model file, and outputting analysis result data; and calling the basic model to carry out design scheme evaluation and iterative optimization based on the analysis result data until an optimized model file meeting the design requirement is generated. The method can be applied to intelligent design and automatic manufacturing of self-adaptive wing structures of aerospace crafts.

The invention discloses a multi-objective optimization method for the form of an urban block, and the method comprises the following steps: S100, constructing a parameterized model, carrying out the experimental design, defining a group of geometric parameters used for describing the form of the urban block, and generating a plurality of sample points in the value range of the geometric parameters through employing a sampling method; s200, bidirectional coupling numerical simulation is executed, a data set is generated, at least one thermal comfort evaluation index and one air quality evaluation index are output, and therefore the data set containing input parameters and output indexes is formed; step S300, constructing and verifying a machine learning agent model to obtain an agent model capable of rapidly predicting the evaluation index; step S400, executing multi-objective optimization search, and automatically searching to enable the thermal comfort evaluation index and the air quality evaluation index to simultaneously achieve a better Pareto optimal solution set; and step S500, analyzing and outputting an optimization result, extracting an optimization value range of the geometric parameters, and generating a design guide.

The invention discloses an injection molding method of an anti-aging simulation plant. The injection molding method comprises the following steps: preparing raw materials; uniformly mixing the raw materials according to a set material proportioning model so as to obtain anti-aging proportioning raw materials exceeding a preset threshold value; according to the shape and the size of the simulation plant, through a system integrated with an intelligent design engine, design data are extracted, and a design mold is generated; the proportioned raw materials are heated in a heating cylinder of an injection molding machine to be in a molten state, and molten plastic is obtained; the molds are firmly clamped together; injecting the molten plastic into the cavity of a mold under a predetermined pressure; a preset pressure of a preset time is kept in the cavity; cooling and solidifying the material in the mold to form the shape of the mold; and opening the mold to obtain the formed simulation plant. According to the technical scheme, the service life of the anti-aging simulation plant is prolonged, and the safety of the anti-aging simulation plant is improved by optimizing the raw material ratio, accurately controlling the injection molding process and other key steps.

Described herein is a computer implemented method for generating a plurality of synthesised designs based on a selected design that includes a first element. The method includes accessing design generation data that includes source data and field association data. The source data defines a first field and a first design generation record that defines a first content item that is an image-type content item and that is associated with the first field. The field association data defines a first association between the first field and the first element. The method further includes generating a first synthesised design that is based on the selected design and that is generated such that the first element of the selected design is updated in the first synthesised design using the first content item.

The invention discloses an AIGC technology-based indigenous building space design generation method, and relates to the field of indigenous buildings, the method comprises S1 and S2, the S1 comprises data acquisition, data cleaning and processing, feature extraction and selection, design scheme, design suggestion generation, design scheme optimization, cooperative work and feedback, and design output; s2, space design generation preprocessing, target building module generation, target building floor height determination, space scene generation, scheme building windowing position analysis and windowing area determination are included; the AIGC technology is used for conducting intelligent design, effective data are provided for local buildings, the design accuracy and efficiency can be improved, human errors can be reduced, the design quality and sustainability can be improved, and the situation that due to the fact that the building distance is unqualified or the planning layout design is unreasonable, the construction cost is lowered can be avoided. And the problems of disassembly, rework and the like after a project is built are solved.

The invention discloses a traditional building micro-reconstruction performance improving system based on machine learning. According to the method, the inherent contradiction between traditional building protection and performance improvement is effectively broken through an intelligent multi-dimensional data fusion and generative design technology. A holographic information model constructed by the multi-source data acquisition module completely retains historical genes and space passwords of a building, and provides an accurate space-time reference for subsequent transformation; a two-channel analysis mechanism of the performance evaluation module brings physical structure safety and cultural value continuity into a unified evaluation system, and ensures that a transformation scheme not only meets modern use requirements but also inherits and builds wisdom. The pattern generation and space reconstruction technology of the module is creatively designed, the limitation of traditional craftsman experience inheritance is broken through, contemporary expression of traditional building languages is achieved in the digital twinning environment, and historical style protection is changed from passive limitation to active innovation.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The invention relates to the technical field of electric power iron tower full-life-cycle carbon footprint accounting, provides an electric power iron tower full-life-cycle carbon footprint accounting method and system, and aims to solve the problems of low efficiency and poor accuracy of electric power iron tower full-life-cycle carbon footprint accounting in the prior art. The method comprises the steps that an electric power iron tower component library is constructed, and a design stage carbon accounting benchmark is generated in combination with a building information model, a steel model, a welding process and anti-corrosion coating parameters; capturing assembly installation deviation and splicing gaps in real time, calculating material loss caused by construction errors, and converting the material loss into carbon emission increment; loading data by using a digital twinning technology, adjusting a material loss early warning threshold value, and updating design data; based on the corrected data, icing load deformation and corrosion expansion in the operation and maintenance stage are analyzed, and a full-life-cycle carbon footprint report is generated in combination with the updated stress distribution data. According to the technical scheme provided by the invention, the efficiency and accuracy of the full-life-cycle carbon footprint accounting of the electric power iron tower can be improved.

The invention discloses a bridge substructure modeling scheme generation method and device, equipment and a medium, and relates to the technical field of bridge engineering, and the method comprises the steps: obtaining bridge superstructure design input parameters, constructing substructure design task information, and generating design parameters through big language model reasoning; and a modeling script is automatically generated, and structural response calculation is carried out. Different modeling strategies are judged and executed according to the calculation result, finally, a target modeling scheme meeting the requirement is intelligently generated, design parameter generation and automatic modeling of the bridge substructure in a non-standard bridge type scene are achieved, and the design efficiency and accuracy are improved.